In online social networking sites, gaining popularity has become important. The more popular a company is, the more profits it can make. A way to measure a company's popularity is to check how many likes it has (e.g., the company's number of likes in Facebook). To instantly and artificially increase the number of likes, some companies and business people began hiring crowd workers (aka fake likers) who send likes to a targeted page and earn money. Unfortunately, little is known about characteristics of the fake likers and how to identify them. To uncover fake likers in online social networks, in this work we (i) collect profiles of fake likers and legitimate likers by using linkage and honeypot approaches, (ii) analyze characteristics of fake likers and legitimate likers, (iii) propose and develop a fake liker detection approach, and (iv) thoroughly evaluate its performance against three baseline methods and under two attack models. Our experimental results show that our cassification model significantly outperformed the baseline methods, achieving 87.1% accuracy and 0.1 false positive rate and 0.14 false negative rate.

There has been much research conducted into the phenomenon of online social networking. However, there has not been enough research conducted to establish its affect on our overall communication patterns. This research study focuses on the way in which Facebook is redefining perceptions of public and private communication. Using the current body of research paired with a varied theoretical backing, this research establishes Facebook's affect on the communication of college students while also noting how the users affect the way this medium is used. Focus groups at a private University were conducted to establish current uses and perceptions of Facebook and how college students utilize the site to communicate. The research discovered that through Facebook, a new version of confessing one's thoughts has been established and intensified. Also, the research discovered that students had difficulty in defining their own versions of private and public information, but they understood that the line between the two is no longer distinct.

A research report submitted to the Faculty of Humanities, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Art in International relations, 2017 / This research project investigates public opinions about South African mall robberies discussed on Twitter. Using the principles of discourse and multimodal analysis, it provides critical insights constructed from the represented narratives of select, proposed middle-class consumers illustrating distinct sentiments about malls, crime and shopping. Malls are empirical objects that have been trivialised as ordinary and mundane consumer sites, devoid of any sociological significance embedded within the daily practices of shopping. This paper makes the argument that when contested by criminal activity, malls become valuable sites for critical enquiry towards gaining a deeper understanding of what these shopping attitudes mean within a post-apartheid, South African consumer landscape. The central issue of crime threatening public safety at malls diverges into an array of thematic discussions, revealing distinct indoctrinations surrounding apartheid’s iniquitous system of racial and social engineering. This study’s principle argument makes the claim that anxieties concerning public safety are only the tip of the iceberg, and this serves as an entry point into a discourse contesting exclusive shopping rights above constitutional equality for all. The test tube of mall robberies mixes desirable pleasures and humanitarian moralities together and creates a volatile cocktail of conflicting, consumer aspirations. In short, the public discourse of mall crimes is about maintaining self-entitled spaces of exclusivity within a desperate socioeconomic climate. This study concludes with questions and considerations raised by these authors which could springboard into opportunities for future inquiry. / XL2018

The explosive growth of Online Social Networks (OSNs) over the past few years has redefined the way people interact with existing friends and especially make new friends. OSNs have also become a great new marketplace for trade among the users. However, the associated privacy risks make users vulnerable to severe privacy threats. In this dissertation, we design protocols for private distributed social proximity matching and a private distributed auction based marketplace framework for OSNs. In particular, an OSN user looks for matching profile attributes when trying to broaden his/her social circle. However, revealing private attributes is a potential privacy threat. Distributed private profile matching in OSNs mainly involves using cryptographic tools to compute profile attributes matching privately such that no participating user knows more than the common profile attributes. In this work, we define a new asymmetric distributed social proximity measure between two users in an OSN by taking into account the weighted profile attributes (communities) of the users and that of their friends’. For users with different privacy requirements, we design three private proximity matching protocols with increasing privacy levels. Our protocol with highest privacy level ensures that each user’s proximity threshold is satisfied before revealing any matching information. The use of e-commerce has exploded in the last decade along with the associated security and privacy risks. Frequent security breaches in the e-commerce service providers’ centralized servers compromise consumers’ sensitive private and financial information. Besides, a consumer’s purchase history stored in those servers can be used to reconstruct the consumer’s profile and for a variety of other privacy intrusive purposes like directed marketing. To this end, we propose a secure and private distributed auction framework called SPA, based on decentralized online social networks (DOSNs) for the first time in the literature. The participants in SPA require no trust among each other, trade anonymously, and the security and privacy of the auction is guaranteed. The efficiency, in terms of communication and computation, of proposed private auction protocol is at least an order of magnitude better than existing distributed private auction protocols and is suitable for marketplace with large number of participants.

Network data, particularly social network data, is widely collected in the context of interactions between users of online platforms, but it can also be observed directly, such as in the context of behaviours of animals in a group living environment. Such network data can reveal important insights into the latent structure present among the nodes of a network, such as the presence of a social hierarchy or of communities. This is generally done through the use of a latent variable model. Existing network models which are commonly used for such data often aggregate the dynamic events which occur, reducing complex dynamic events (such as the times of messages on a social network website) to a binary variable. Methods which can incorporate the continuous time component of these interactions therefore offer the potential to better describe the latent structure present. Using observed interactions between mice, we take advantage of the observed interactions’ timestamps, proposing a series of network point process models with latent ranks. We carefully design these models to incorporate important theories on animal behaviour that account for dynamic patterns observed in the interaction data, including the winner effect, bursting and pair-flip phenomena. Through iteratively constructing and evaluating these models we arrive at the final cohort Markov-Modulated Hawkes process (C-MMHP), which best characterizes all aforementioned patterns observed in interaction data. The generative nature of our model provides evidence for hypothesised phenomena and allows for additional insights compared to existing aggregate methods, while the probabilistic nature allows us to estimate the uncertainty in our ranking. In particular, our model is able to provide insights into the distribution of power within the hierarchy which forms and the strength of the established hierarchy. We compare all models using simulated and real data. Using statistically developed diagnostic perspectives, we demonstrate that the C-MMHP model outperforms other methods, capturing relevant latent ranking structures that lead to meaningful predictions for real data. While such network models can lead to important insights, there are inherent computational challenges for fitting network models, particularly as the number of nodes in the network grows. This is exacerbated when considering events between each pair of nodes. As such, new computational tools are required to fit network point process models to the large social networks commonly observed. We consider online variational inference for one such model. We derive a natural online variational inference procedure for this event data on networks. Using simulations, we show that this online learning procedure can accurately recover the true network structure. We demonstrate using real data that we can accurately predict future interactions by learning the network structure in this online fashion, obtaining comparable performance to more expensive batch methods.

Indiana University-Purdue University Indianapolis (IUPUI) / The impact of new technology on the current presidential campaign has prevailed. A small but fundamental change quietly took place in the candidate’s website—Barack Obama’s official campaign blog. The campaign is now operating two sub-blogs at the same time, the Obamablog (also known as the Obama HQ blog) and the Community Blog. The former becomes a must-have tool in a political campaigner’s strategy, whereas the latter, which allows visitors to actually write, publish, and manage posts, is novel. This new function seems an audacious step up from the rest of the blogs of this kind since the 2004 presidential election because it gives citizen users freedom to express their own ideas that could put the campaign on an impromptu situation to respond.

Indiana University-Purdue University Indianapolis (IUPUI) / Tattoos, a permanent body modification that has frequently been associated with deviance and lower class sub-cultures, have become increasingly popular in the United States since the early 1990’s. In my thesis I examine the shared worldviews of individuals who obtain tattoos by conducting an analysis of six internet communities that promote this sub-culture in order to understand how cultural influences provide the impetus to create self-identity through inscribing the body. I will argue that individuals who commit to a permanent tattoo may be motivated by the need to create self identity.

This study examined college student motivations for using social networking sites for educational purposes. Motives were examined through the uses and gratifications approach. If we can determine student motivations for using social networking sites, perhaps we can determine a way to successfully implement social networking sites into the classroom. By adding the concept of satisfaction, we can also determine if students will use the sites again. If students are satisfied with educational social networking site use, they will return to these sites for educational purposes again. Data was collected by giving a questionnaire to undergraduate students that assessed social networking site use, as well as motivations for and satisfaction with use. For general uses, students were motivated to use social networking sites for relationship maintenance, passing time, and information seeking purposes. Overall, students were satisfied with their use of the sites. For educational uses, students were motivated to use the sites for relationship maintenance and information seeking purposes. Overall, students are not satisfied with their use of these sites for educational purposes. Theoretical and practical implications of these findings are discussed.

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